This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of Training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the Training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
標簽： approximation demonstrates capabilities Function
上傳時間： 2014-01-01
上傳用戶：zjf3110
Blind Equalizer 的演算法主要是利用CMA及 LMS 的配合，當CMA將EYE打開，使訊號趨近于正確值，就切換到LMS，利用Slicer的輸出當作Training sequence來調整Equalizer的系數，而Carrier Recovery 的部份，則是將phase error track出來
上傳時間： 2013-12-28
上傳用戶：it男一枚
C＋＋編寫的機器學習算法 Lemga is a C++ package which consists of classes for several learning models and generic algorithms for optimizing (Training) the models
標簽： consists learning classes package
上傳時間： 2014-01-21
上傳用戶：wangchong
一些數據庫的實例。共12章。如第八章： 第8章數據庫環境的建立 1. 用MISDBA用戶登錄MISDB數據庫。 2. 在ISQL中，輸入第8章提供的SQL語句；或者根據表8-1至表8-4在SQL Explorer中自行創建數據表。 3. 根據表8-5至表8-7設置初始數據，另外需要在PERSON數據表中設置一個具有培訓管理系統管理權限的用戶（ID=’PXC’，PASSWD=’PASSWORD’，AUTHORITY=’6’，STATE=’F’）和用于外派培訓的用戶（ID=’PXCOUT’，NAME=’外派培訓’）。 4. 修改Admin源程序中的數據庫連接屬性，并且重新編譯Training.exe。 5. 修改Client源程序中數據庫連接屬性，并且重新生成html文件和cab文件，然后將這兩個文件拷貝到web服務器指定目錄中。
上傳時間： 2014-01-09
上傳用戶：zxc23456789
pop3代理服務器源代碼One of the most powerful features of Pop3 Agent is a naive Bayes filter, that is capable of recognizing spam e-mails after appropriate Training. Pop3 Agent uses an embedded Firebird database server. Of course, you can configure Pop3 Agent to work with an existing server if there is another Interbase/Firebird installation available in your network. Open the Pop3 Agent home directory, delete or rename gds32.dll and ib_util.dll, and set the INI file parameter FBembedded=0.
標簽： features powerful filter Agent
上傳時間： 2014-01-10
上傳用戶：yoleeson
This code in this directory implements the binary hopfield network. Source code may be found in HOPNET.CPP. A sample Training file is H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST, H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the screen and a file, ARCHIVE.LST.
標簽： code implements directory hopfield
上傳時間： 2014-01-16
上傳用戶：123啊
palm編成,這種書很少,有興趣看看 Title: Palm Programming: The Developer s Guide URL: http://safari.oreilly.com/JVXSL.asp?x=1&mode=section&sortKey=rank&sortOrder=desc&view=book&xmlid=1-56592-525-4&open=false&srchText=palm+programming&code=&h=&m=&l=1&catid=&s=1&b=1&f=1&t=1&c=1&u=1&page=0 ISBN: 1-56592-525-4 Author: Julie McKeehan/ Neil Rhodes Publisher: O Reilly Page: 478 Edition: 1st edition (December 1998) Catalog: PDA programming / Palm Format: pdf Size: 2.06M Supplier: Summary: Emerging as the bestselling hand-held computers of all time, PalmPilots have spawned intense developer activity and a fanatical following. Used by Palm in their developer Training, this tutorial-style book shows intermediate to experienced C programmers how to build a Palm application from the ground up. Includes a CD-ROM with source code and third-party developer tools
標簽： Programming Developer oreilly safari
上傳時間： 2013-12-10
上傳用戶：litianchu
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of Training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標簽： approach combines particle tracking
上傳時間： 2016-01-02
上傳用戶：yy541071797
Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) Training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.
標簽： Hidden_Markov_model_for_automatic speech_recognition implements left-right
上傳時間： 2016-01-23
上傳用戶：569342831
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing Training from millions of examples by hundreds of features in a reasonable time/memory.
標簽： meta-learning classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
